The present invention relates to a method of generating a defect classifier, which classifies foreign matter and defects arising on a semiconductor wafer or other sample in a semiconductor manufacturing process, a method of classifying defects with the generated defect classifier, and a system for such defect classification.
A semiconductor device is manufactured by subjecting a wafer, which serves as a substrate, to a plurality of processes such as exposure, development, and etching. After completion of a predetermined processing step of a plurality of processing steps, an optical or SEM (Scanning Electron Microscope) type foreign matter inspection machine or pattern inspection machine is used to conduct an inspection for the purpose of determining the location and size of a defect. The number of detected defects depends on the manufacturing process condition. However, it may range from several hundred to several thousand per wafer. Therefore, the defect inspection machines are required to offer a high defect detection speed. The inspection machines for detecting defects are hereinafter generically referred to as defect detection machines.
After inspection by such a defect detection machine, an optical or SEM-type defect review machine having a higher image magnification may be used to conduct a fine reinspection on defects detected by the defect detection machine. When time limitations are considered, however, it is not practical to conduct a fine inspection on all defect samples detected by the defect detection machine. Therefore, a set of defects detected by the defect detection machine is first sampled, and then its subset is subjected to a fine inspection. The defect inspection machines for conducting the above defect review inspection are hereinafter generically referred to as defect review machines. Further, the defect detection machines, defect review machines, SPMs (Scanning Probe Microscopes), elemental analysis machines, and other similar inspection machines are collectively referred to as defect inspection machines.
Some of the developed defect review machines incorporate a function of automatically acquiring an defect image enlargement in accordance with the defect location information derived from a defect detection machine, that is, the ADR (Automatic Defect Review) function, and a function of acquiring detailed information about a defect, including its size, shape, and texture (surface pattern), from the defect image enlargement and automatically determining the type of the defect, that is, the ADC (Automatic Defect Classification) function. Meanwhile, some of the developed defect detection machines incorporate a function for rough defect classification, which can be exercised without sacrificing the high processing speed. This classification function is called “RT-ADC (Real Time-ADC)”.
As regards the methods for automatically classifying defects in accordance with various inspection information, a variety of techniques have long been studied as a multivariate analysis method in a pattern recognition field.
One classical methodology is a method called “rule-based classification”. This methodology extracts various image feature amounts from an image targeted for. classification, judges the image feature amount values in accordance with “if-then” rules incorporated into the system, and categorizes a defect into one of defect classes. The rule-based classification method deals with fixed defect classes and classification rules and cannot flexibly respond to user requests. However, it is advantageous in that it can be used immediately after production process startup because it does not require any teaching data.
Another classical methodology is a method called “learning classification”. This methodology gathers teacher images in advance and then learns them to optimize the classification rules (e.g., neural net). The learning classification method provides flexible classification in compliance with user requests. However, it is generally necessary to gather a large amount of teaching data in order to obtain satisfactory performance. Therefore, it cannot practically be used at the time of production process startup. If only a small amount of teaching data is used, it is understood that the performance deteriorates because excessive learning occurs. Excessive learning is a phenomenon in which the learning is excessively adapted to teaching data.
As a combination of the above rule-based classification and learning classification methods, an automatic defect classification method, which can be adapted uniformly in a hybrid, is disclosed by Japanese Patent Laid-open No. 2001-135692.
The conventional technologies for defect classification are also disclosed by Japanese Patent Laid-open Nos. 1999-344450, 2001-93950, 2001-127129, 2001-256480, 2001-331784, 2002-14054, and 2002-90312.
However, even when a user's classification request does not comply with the classification results generated by a defect classifier, which is derived from the above rule-based classification method, learning classification method, or their combination, it is not easy to correct the system's internal classification standard. More specifically, if the meanings of various attributes used as classification judgment criteria are vague when the above rule-based classification method is used, it is difficult for the user to customize various attributes in compliance with the user's own classification request by, for instance, making attribute selections and defining threshold values. Further, if many attributes are inadvertently readied as feature amounts in a situation where the classification rules are automatically generated during the use of the learning classification method, the degree of leaning freedom increases so that excessive learning may occur as the learning is excessively adapted to a small mount of teaching data. To avoid such a problem, an increased number of teaching samples are necessary. The same problem arises when the rule-based classification and learning classification methods are combined. Further, it is also necessary to determine the proper configuration for such a combination.
The defect attributes that can be derived from a defect inspection machine include image feature amounts, defect coordinates, composition analysis results, manufacture initiation history data, machine QC (Quality Control) data, information with respect to the distribution of defect locations detected on a wafer, and the number of defects. In some cases, it is also possible to reference the attributes obtained from a plurality of different types of defect inspection machines such as an optical or SEM foreign matter inspection machine, pattern inspection machine, defect review machine, SPM, and elemental analysis machine. Automatic defect classification is performed using the above attributes as judgment criteria. However, it is not easy for the user to handle a large amount of attribute data properly and generate a defect classifier that conforms to an expected defect classification standard.